As AI and machine learning become core to biomedical research, the challenge is no longer whether models can be built—it’s whether organizations can operate them reliably, securely, and at a global scale. We will explore how cloud-native compute, scalable model-serving architectures, multimodal data platforms, and rigorous security and compliance frameworks come together to form an integrated, end-to-end capability for AI in biomedicine.

In this session, we will discuss a cloud-native AI platform that democratizes access to AI for business teams, scientists, and analysts across the organization. We will go deeper into how we unified data, domain knowledge, and secure model orchestration to create a platform where teams can rapidly prototype, validate, and operationalize AI applications—from natural-language analytics and workflow automation to decision-support copilots and RAG-powered knowledge systems. The talk will highlight the architectural and governance principles that ensure reliability, safety, and compliance in a regulated environment, while harnessing AI without deep machine learning expertise resulting in accelerated innovation, improvement in operational efficiency, and unlocking of tangible business value by making AI a practical, self-service capability across the organization.

Agentic AI can transform how enterprises operate—but only with the right foundation. This session explores what it means to be agentic AI-ready, covering control tower and responsible AI, agent identity/access and security, data ontology, and FinOps. We will introduce an agentic orchestration framework and clarify platform, data, application, and business agents, giving you practical guidance to design a scalable, secure, and value-driven agentic AI ecosystem.

AI has transformed protein structure prediction; yet many R&D teams still struggle with fragmented tools, inconsistent infrastructure, and steep learning curves. We present a unified platform that combines the best public, commercial, and proprietary AI models within a seamless hybrid-cloud environment. With intuitive visualization, robust API integration, and integrated access to diverse prediction methods, the platform enables scientists to move from raw sequence to actionable insights for therapeutic design and optimization, delivering results faster, more reliably, and at scale.

Large foundation model training demands access to diverse—and geographically distributed—computation and data resources. Coordinating access to these resources requires building container images, managing training jobs, monitoring job progress, and managing data and provenance across multiple storage systems and computing clusters. We will describe how cloud services on the Globus platform are being used to orchestrate this process and highlight use cases from computational biology and other scientific domains.

• How are knowledge graphs supporting reliable scientific intelligence
• Lessons from early deployments
• Remaining challenges and barriers to scale​

• Learn how leading global pharma companies are applying quantum principles to real-world research. 
• Discover the quantum companies transforming traditional processes around drug development and drug discovery.
• Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.​

• Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
• What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
• What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?​

• Where are the highest-value collaboration opportunities between academia/TTOs and industry (biopharma, startups, AI/data, investors), and what makes them actually work?
• What types of partnerships are most effective in practice (licensing, co-development, startup creation, sponsored research), and how do both sides evaluate quality and fit quickly?
• What specific formats, access points, or structures at the Bio-IT event would actually drive follow-up, partnerships, and deal-making?

• How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
• What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
• How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?
  

• How real-world data is being used alongside clinical and experimental data
• Challenges in standardization, access, and regulatory acceptance
• Opportunities to improve outcomes, access, and long-term patient insights

• Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work 
• How structured experimental memory makes past decisions, failures, and context reusable across teams
• How organizations can make scientific knowledge reusable for both humans and AI

• How to think strategically about IP early in biotech and AI-driven research
• Common pitfalls in patent and trademark applications and how to avoid them
• Navigating ownership, protection, and commercialization of innovations
  

• Why do current workflow systems fail most scientists in practice? 
• Can no-code and AI-driven workflows meet standards for reproducibility, validation, and clinical readiness? 
• What would a “production-ready” workflow ecosystem actually look like across teams and organizations?

• The gap between AI ambition and the reality of data foundations, platforms, and operating models
• What “AI‑ready data” actually means in practice—beyond pilots, dashboards, and proofs of concept 
• Where AI initiatives most often break down: data integration, data quality, metadata, and governance at scale​

Biotech AI is only as powerful as the data it can access. This talk presents an end-to-end platform for automatically moving lab data to the cloud, making it searchable, governed, versioned, and ready for machine learning. By linking instrument data, cloud storage, dataset packaging, and reproducible MLOps workflows, teams can reduce manual transfer, improve traceability, and scale AI-driven research.

We present a systems-focused pipeline that converts population-scale biosensor-based actigraphy and PPG data (i.e., biosensor data) into reproducible, high-throughput training datasets for multimodal digital health foundation models. We implement a two-stage design: preprocessing and chunking the time series into binned window sizes using scalable EKS clusters, followed by distributed multi-GPU transformer training with a GPU-aware DataLoader and a time-series database to reduce I/O contention, and enable fast metadata-driven retrieval across multi-terabyte storage. In brief, this work emphasizes the infrastructure and data-engineering trade-offs, as well as the operational patterns required to convert siloed time series into reproducible, high-throughput training pipelines.